The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index.
Learning to Track with Object Permanence
Open accessAbstract
Tracking by detection, the dominant approach for online multi-object tracking, alternates between localization and association steps. As a result, it strongly depends on the quality of instantaneous observations, often failing when objects are not fully visible. In contrast, tracking in humans is underlined by the notion of object permanence: once an object is recognized, we are aware of its physical existence and can approximately localize it even under full occlusions. In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning. We build on top of the recent CenterTrack architecture, which takes pairs of frames as input, and extend it to videos of arbitrary length. To this end, we augment the model with a spatio-temporal, recurrent memory module, allowing it to reason about object locations and identities in the current frame using all the previous history. It is, however, not obvious how to train such an approach. We study this question on a new, large-scale, synthetic dataset for multi-object tracking, which provides ground truth annotations for invisible objects, and propose several approaches for supervising tracking behind occlusions. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI and MOT17 datasets thanks to its robustness to occlusions.
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“However, the projection of non-linear 3D motion [50] into the 2D image domain still poses a challenging problem for many models.”
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Research works printed but not linked: 1
- 2021, Parallel domain
Cited by
Sorted by citations
ByteTrack: Multi-object Tracking by Associating Every Detection Box
Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., & Wang, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_1 · Cited by 3,040
StrongSORT: Make DeepSORT Great Again
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“On the KITTI dataset, we use the same detection results as PermaTrack [46] and OC-SORT [7] for fair comparison.”
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Cao, J., Pang, J., Weng, X., Khirodkar, R., & Kitani, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00934 · Cited by 1,115
TrackFormer: Multi-Object Tracking with Transformers
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“However, the projection of non-linear 3D motion [49] into the 2D image domain still poses a challenging problem for many models.”
BackgroundMethodInfluentialTransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking
Chu, P., Wang, J., You, Q., Ling, H., & Liu, Z. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00485 · Cited by 286
Metadata
- Authors
- Tokmakov, P., Li, J., Burgard, W., & Gaidon, A.
- Affiliations
- Toyota Motor Corporation (Switzerland) (CH)
- University of Freiburg (DE)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10840-10849
- OpenAlex
- W3142617676
- Cited by
- 226
- References
- 74
- Citations by year
- 2026: 14 · 2025: 43 · 2024: 72 · 2023: 48 · 2022: 46 · 2021: 3
- FWCI
- 13.74
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE/CVF International Conference on Computer Vision (ICCV)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multi-target tracking, object permanence, occlusion handling, joint detection and tracking, spatiotemporal memory, end-to-end tracking, KITTI, MOT17
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